You can create a pipeline that reads a data table in Microsoft SQL Server (MSSQL). The read pipeline imports the data from MSSQL to Anaplan Data Orchestrator. It then uses the imported data to create or update a source dataset.

Prerequisites

You need a connection to MSSQL to create a pipeline. Make sure you meet the prerequisites in these sections before you create a connection to MSSQL and create a read pipeline.

Create a connection to MSSQL

Use the MSSQL connector in Data Orchestrator to create a connection to MSSQL. The MSSQL connector also supports connections to Azure SQL.

You will need your MSSQL or Azure credentials. View the MSSQL or Azure documentation for more information about your credentials.

To create a connection:

  1. Select Data Orchestrator from the top-left navigation menu.
  2. Choose a dataspace from the list.
  3. Select Connections on the left-side panel.
  4. Select Create connection.
  5. Select the Microsoft SQL Server (MSSQL) connector and then select Next.
    If you can't find the connector, enter a search term in the Find... field.
  6. Enter these details on the Connection details screen, and then select Next:
    • Name: Create a name for your connection. The name can contain alphanumeric characters and underscores.
    • Description: Enter a description about your connection.
  7. Enter your MSSQL credentials on the Connection credentials screen, and then select Next.
    For information about the fields on the Connection credentials screen, see SSH tunneling options for MSSQL connections.
  8. After the connection test is complete, select Done.

Create a read pipeline

Use the MSSQL connection you created to create the read pipeline. The read pipeline uses the connection to import data from a table in MSSQL to a source dataset in Data Orchestrator.

To create a read pipeline:

  1. Select Data Orchestrator from the top-left navigation menu.
  2. Choose a dataspace from the list.
  3. Select Pipelines from the left-side panel.
  4. Select Create pipeline.
  5. Enter a Name for your pipeline and then select Create.
    You are taken to the pipeline designer view.
  6. Select the Source icon, and then complete these steps in the right-side panel:
    1. Select MSSQL from the Connection type dropdown. 
    2. Select the MSSQL connection you created from the Choose connection dropdown.
    3. Enter a Label that displays as the source name in the designer view.
    4. Select a Source location, and then select a table in MSSQL that you want to connect.
  7. Optionally, select the add icon that appears between the Source and Sink nodes.
    You can add steps to your pipeline to process data.
  8. Select the Sink icon, and then complete these steps in the right-side panel:
    1. Select Anaplan from the Connection type dropdown.
    2. Select Datasets from the Choose connection dropdown.
    3. Select Target location > Table, and choose a dataset destination:
      • Choose existing dataset: If you choose this option, you will be asked to select an existing source dataset.
      • Create new dataset: If you choose this option, you will be asked to enter a Name and Description for the new dataset.
    4. Choose a write option for the dataset.
      If you chose Create a new dataset in the previous step, the write option only applies to later pipeline runs if you update the table in MSSQL.
      • Upsert: Updates the existing rows and adds new rows if needed.
      • Append: Adds new data to the dataset without overwriting existing data.
      • Replace: Replaces all existing data with the new data being extracted, and overwrites any previous data.
    5. Review the source data from the MSSQL table that's being imported to the dataset, and then select Done.
  9. Select Publish, and then select Run to execute the data transfer.

The dataset displays in the Source datasets screen in Data Orchestrator. If you don't see the dataset, refresh the screen.

Run the pipeline with a Workflow

After you create the pipeline, optionally, you can choose to run the pipeline as part of an Anaplan Workflow. This enables you to automate data transfers based on a schedule, or trigger them manually as part of a larger sequence of tasks.